Year-over-year SLA compliance for first response and resolution across 67,521 tickets. Broken down by priority to show where performance is improving and where it is slipping. Generated by AI via Proxuma Power BI MCP server.
Year-over-year SLA compliance for first response and resolution across 67,521 tickets. Broken down by priority to show where performance is improving and where it is slipping. Generated by AI via Proxuma Power BI MCP server.
The data covers the full scope of Autotask PSA records relevant to this analysis, broken down by the key dimensions your team needs for day-to-day decisions and client reporting.
Who should use this: Service delivery managers, operations leads, and MSP owners tracking service quality
How often: Weekly for operational adjustments, monthly for client reporting, quarterly for contract reviews
Year-over-year SLA compliance for first response and resolution across 67,521 tickets. Broken down by priority to show where performance is improving and where it is slipping. Generated by AI via Proxuma Power BI MCP server.
first_response_met and resolution_met fields in the BI_Autotask_Tickets table. These are boolean fields (stored as int64) that indicate whether each ticket met its SLA target. Percentages are calculated by dividing met tickets by total tickets per year and priority.
First response and resolution SLA rates plotted across 2024, 2025, and early 2026
The two lines crossed between 2024 and 2025. First response compliance dropped from 61.8% to 49.4%, while resolution climbed from 48.1% to 68.7%. In 2024, you were better at picking up tickets quickly but worse at resolving them on time. By 2025, the opposite was true.
Early 2026 data (2,164 tickets) shows first response recovering to 62.6% and resolution holding steady at 67.9%. The sample size is still small, but the direction is positive on both fronts.
EVALUATE ROW("CSATAvg", [CSAT - Average Rating], "CSATLastYear", [CSAT - Average Rating - Last Year], "Ratings", [CSAT - Total Ratings])First response and resolution SLA rates per priority across 2024, 2025, and 2026
| Priority | FR% 2024 | FR% 2025 | FR% 2026 | Res% 2024 | Res% 2025 | Res% 2026 | FR Trend |
|---|---|---|---|---|---|---|---|
| P1 Kritiek | 63.6% | 28.8% | 22.4% | 50.7% | 57.1% | 75.3% | Collapsing |
| P2 Hoog | 86.0% | 35.1% | 5.5% | 93.0% | 92.0% | 90.9% | Collapsing |
| P3 Normaal | 59.8% | 26.1% | 25.9% | 76.0% | 56.6% | 51.8% | Declining |
| P4 Laag | 72.0% | 56.1% | 80.9% | 31.1% | 73.5% | 73.8% | Recovering |
| Service/Change | 33.2% | 64.6% | 10.3% | 32.8% | 65.9% | 11.2% | Volatile |
EVALUATE
ADDCOLUMNS(
SUMMARIZECOLUMNS(
'BI_Autotask_Tickets'[priority_name],
"TotalTickets", COUNTROWS('BI_Autotask_Tickets'),
"FirstResponseMet", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[first_response_met] + 0 = 1),
"ResolutionMet", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[resolution_met] + 0 = 1)
),
"FirstResponsePct", DIVIDE([FirstResponseMet], [TotalTickets]),
"ResolutionPct", DIVIDE([ResolutionMet], [TotalTickets])
)
ORDER BY [TotalTickets] DESCThe declining first response story told priority by priority
The first response chart tells a clear story: the higher the priority, the worse the decline. P2 Hoog went from 86.0% in 2024 to 5.5% in 2026. That is not a gradual slide. That is a process that broke.
P1 Kritiek shows a similar pattern, dropping from 63.6% to 22.4%. These are the tickets that matter most to clients, and they are the ones most likely to miss their first response window.
P4 Laag is the only priority that improved, rising from 72.0% to 80.9%. This makes sense if your dispatch team is handling lower priorities promptly while higher-priority tickets get stuck in triage or escalation queues.
P3 Normaal plateaued around 26% after its initial drop. It is not getting worse, but it is not recovering either.
Resolution SLA compliance is improving across most priorities
Resolution tells a different story from first response. P2 Hoog has held above 90% for three consecutive years. Whatever your resolution process looks like for high-priority tickets, it works.
P1 Kritiek climbed from 50.7% to 75.3%, the biggest improvement in the dataset. Critical tickets are getting resolved within their SLA window more consistently.
P4 Laag made a massive jump from 31.1% to 73.8%. That suggests the team stopped deprioritizing low-priority tickets to the point where they breached SLA. It could also reflect adjusted SLA targets.
The exception is P3 Normaal, which dropped from 76.0% to 51.8%. This is the one priority where resolution is going in the wrong direction.
EVALUATE
ROW(
"OverallFRPct", DIVIDE(
COUNTROWS(FILTER('BI_Autotask_Tickets', 'BI_Autotask_Tickets'[first_response_met] + 0 = 1)),
COUNTROWS('BI_Autotask_Tickets')
),
"OverallResPct", DIVIDE(
COUNTROWS(FILTER('BI_Autotask_Tickets', 'BI_Autotask_Tickets'[resolution_met] + 0 = 1)),
COUNTROWS('BI_Autotask_Tickets')
),
"TotalTickets", COUNTROWS('BI_Autotask_Tickets')
)Why first response is declining while resolution is improving
The data shows two SLA metrics moving in opposite directions. That is unusual, but the explanation is straightforward once you look at what each metric actually measures.
First response depends on dispatch speed. It measures how quickly someone picks up a ticket and sends the first meaningful reply. When ticket volume jumped from 16,857 (2024) to 48,500 (2025), dispatch could not keep up. Volume nearly tripled, but the team did not triple. The result: tickets sat in the queue longer before anyone touched them.
Resolution depends on how the team handles tickets after pickup. The improvement from 48.1% to 68.7% suggests that once someone starts working on a ticket, they finish it faster than before. This could reflect better documentation, improved tooling, more experienced engineers, or tighter process discipline around open ticket aging.
The net effect: clients wait longer for the first reply, but once work begins, it finishes within the SLA window more often. That is not necessarily a good trade. Clients notice long initial wait times before they notice resolution speed. A slow first response signals neglect, even if the eventual resolution is fast.
The priority breakdown supports this theory. P1 and P2 first response collapsed because those tickets need fast dispatch by definition. When the queue backs up, high-priority tickets suffer the most because their response windows are the shortest. Meanwhile, P4 first response actually improved because those tickets have generous SLA windows that survived the volume increase.
P1 Kritiek went from 63.6% to 22.4%. P2 Hoog went from 86.0% to 5.5%. These are your most urgent tickets. When clients file a critical issue and wait beyond the SLA window for a first response, trust breaks down fast. The volume increase from 2024 to 2025 is part of the cause, but a 3x volume increase should not produce an 80-point drop in P2 compliance. Something in the dispatch or triage process needs investigation.
Overall resolution went from 48.1% to 68.7%. P1 jumped from 50.7% to 75.3%. P4 jumped from 31.1% to 73.8%. The team is closing tickets within target more consistently than a year ago. This is real progress. P2 has held above 90% for three years straight. The one concern is P3 Normaal, which declined from 76.0% to 51.8%.
Ticket volume went from 16,857 in 2024 to 48,500 in 2025. First response rates dropped almost in proportion. The team is handling more tickets and resolving them faster, but the initial pickup is where the bottleneck sits. If volume stays at current levels, first response will not recover without either more dispatch capacity or faster triage processes.
5 priorities based on the findings above
A 5.5% first response rate on P2 tickets is not a trend problem. It is a process failure. Pull the last 50 P2 tickets and check: how long did they sit before assignment? Were they auto-dispatched or manually picked up? Is there a queue bottleneck where P2 tickets get stuck behind lower priorities? Fix the dispatch path before worrying about anything else.
If your SLA clock starts ticking when the ticket is created, you need an automated first response for critical tickets. A templated acknowledgment like "We received your critical issue and a technician is being assigned" buys time without lying to the client. It also resets expectations while dispatch catches up. This single change could recover 20-30 points on P1/P2 first response rates.
P3 is the only priority where resolution is getting worse, dropping from 76.0% to 51.8%. This could be a capacity issue (too many P3 tickets) or a complexity issue (P3 tickets are harder than before). Pull the average age and escalation rate for P3 tickets in 2025 vs 2024. If the tickets are sitting longer before resolution, it is a capacity problem. If they are being escalated more, it is a complexity problem.
SLA targets set when you had 16,857 tickets per year may not be realistic at 48,500. That does not mean lowering standards. It means being honest about what your team can deliver at current staffing. If P2 first response was set at 30 minutes when volume was a third of what it is now, consider whether 60 minutes is more realistic while you scale the team.
Resolution went from 48.1% to 68.7% in one year. That is a significant operational improvement. Figure out what caused it. Was it a new documentation system? A shift in how tickets are assigned to L2? Better tooling for common issues? Whatever changed, make sure it is documented and applied consistently. Good results without understanding why they happened are fragile.
Autotask tracks SLA compliance per ticket using the first_response_met and resolution_met fields. These are boolean values that indicate whether the ticket met its SLA target for first response and resolution respectively. Proxuma Power BI pulls this data via the Autotask connector and the AI runs DAX queries to aggregate it by year and priority.
2026 only has 2,164 tickets so far because the year just started. Small samples produce volatile percentages. A single bad week can swing the numbers significantly. Treat 2026 as directional, not definitive. The trends from 2024 to 2025 are more statistically reliable because they cover 16,857 and 48,500 tickets respectively.
A ticket meets its first response SLA when the first technician response (note, email, or status change) happens within the SLA window defined for that ticket's priority and contract. Autotask calculates this automatically based on the SLA plan attached to the client's contract. The first_response_met field stores the result as a boolean.
They measure different things. First response is about dispatch speed: how fast someone picks up the ticket. Resolution is about completion speed: how fast the ticket gets fixed after work begins. A team can get better at fixing tickets while getting worse at picking them up, especially when volume increases faster than staffing. That is exactly what happened here.
Yes. Connect Proxuma Power BI to your Autotask account, add an AI tool (Claude, ChatGPT, or Copilot) via MCP, and ask the same question. The AI writes the DAX queries, runs them against your real data, and produces a report like this in under fifteen minutes.
Industry standards vary, but most MSPs target 80%+ for first response and 85%+ for resolution on P1/P2 tickets. For P3 and P4, 70%+ is common. Anything below 50% on any priority signals a structural problem that needs attention. The specific targets should match what your client contracts promise.
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